A variety of computing configurations and systems may consume relatively large amounts of power. Such systems may include those for cloud computing, Big Data analytics, web services, enterprise services, distributed computing, high performance computing (HPC), and so on. The facility housing such systems can require significant energy.
Distributed computing, HPC systems, and other aforementioned configurations may facilitate scientists and engineers to solve complex science, engineering, and business problems using applications that benefit from high bandwidth, low latency networking, and very high compute capabilities. These systems may also execute data storage and retrieval, perform more straightforward tasks, and so on. Unfortunately, again, systems such as distributed systems, HPC systems, and others, which may have hundreds or thousands of processors, servers, or compute nodes performing tasks, typically consume significant power. Such may be especially problematic in the “Big Data” era. Further, variations in power consumption and issues of power allocation may also be problematic.
The competitive business of data and computing services drives manufacturers in the continuous improvement of their processes and products in order to lower production costs and deliver reliable service. Indeed, as technologies advance in services for data, computing, and telecommunications, a competitive need exists to continuously increase consistency of service and the efficiency of power utilization.
The same numbers are used throughout the disclosure and the figures to reference like components and features. Numbers in the 100 series refer to features originally found in FIG. 1; numbers in the 200 series refer to features originally found in FIG. 2; and so on.